International Scientific Hub

An overview of smart data-driven businesses

2024-04-06T14:39:35+0330

Most organizations and companies invest their financial resources in collecting, storing, securing and analyzing data. Becoming a "data-driven" business is not always easy, and there are likely to be obstacles. It will exist in this way, which is why data and technology alone will not make an organization more successful. Rather, it is necessary to change the way of thinking and efforts of management and employees. Coordinating change and making it happen effectively requires executive support, agility, data efficiency, and a broad, engaged community to ensure that the mission, goals, and needs of the entire business are met. In this research, we will focus on this important topic.

Smart processes in the data-driven supply chain

2024-04-06T15:10:21+0330

The modern supply chain is becoming increasingly complex and global. For businesses, this means that it is critical to identify potential vulnerabilities in the supply chain, address bottlenecks, and respond quickly to supply chain disruptions. The supply chain for businesses is not just a simple and linear chain of activities. Rather, it is a dynamic network of integrated processes, technology, and people. The data-driven approach takes advantage of access to new data sources to help companies manage supply chains. In fact, achieving excellence in supply chain management can provide sustainable growth and greater financial results. In this thesis, the basic components of data-driven supply chains are investigated.

Smart data-driven marketing in Industry 5.0

2024-04-27T23:16:13+0330

Customer data, which is usually collected with the help of various tools, has a significant impact on knowing more customers, improving relationships and providing better services. Data-driven marketing is an approach to optimize brand communication based on customer information. Data-driven marketers use customer data to predict their needs, wants, and behavior. Such information helps develop personalized marketing strategies. Data-based marketing allows the most suitable offer to be presented to the customer at the most suitable time and to establish an effective relationship with them. In the era of transformative technologies and considering the basic challenges of receiving, maintaining and using a large volume of customer data, understanding The basic features of these marketing systems have multiple importance. Therefore, in this chapter, in addition to explaining the difference between data-driven marketing and traditional marketing, an effort is made to examine the basic elements and components of this type of effective retrieval.

Examining the challenges and opportunities of using transformative technologies in Industry 5.0

2024-05-12T20:26:55+0330

Industry 5.0 could be a insurgency in which people and machines discover ways to work together to move forward generation efficiency. If there was no human touch within the revolution within the 4.0 industry, it'll be its center within the fifth industry. Human laborers and worldwide robots together will increment the efficiency of the fabricating world.Therefore, it seems that with the development of Industry 5.0, technologies will take a different form. By approaching human intelligence, in addition to the evolution of technologies, many challenges are always formed in the fields of the future of humanity. People may need to develop completely new skills. Working alongside robots sounds great, but human workers must learn how to work with an intelligent machine, a robot maker. Beyond the soft skills required, technical skills will also be an issue. In this chapter, the category of challenges and benefits of using technologies in Industry 5.0 will be discussed.

Dimensions and main elements of data-driven agriculture

2024-06-09T13:49:22+0330

Big data is a vast collection of structured and unstructured data that can be extracted and used to analyze information and build predictive systems for better decision making. Telecommunications, healthcare, marketing, education and many other applications including There are cases that are widely used in the use of big data in agriculture. Technologies such as livestock monitoring tools, unmanned aerial vehicles and soil sensors are constantly generating large volumes of data to support data-driven agriculture. In this chapter, the most important applications of big data in data-driven agriculture are examined.